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AI in Clinical Decision Support Systems: Promising Applications and Strategies for Managing Data Challenges.

Jennifer E Daly1, Dursun Delen2,3, Zheng Han4

  • 1Office of the Chief of Staff, Eastern Oklahoma VA Health Care System, 1101 Honor Heights Drive, Muskogee, 74401, United States, 1 757-784-5104, 1 757-781-1077.

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This summary is machine-generated.

Artificial intelligence (AI) in clinical decision support systems (CDSSs) improves patient outcomes but faces data access challenges. Addressing data accessibility and protection is crucial for realizing AI

Keywords:
AIartificial intelligencebig dataclinical medical informaticsdecision support systemsdecision-makingdelivery of health caremachine learningpopulation healthprecision medicine

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Area of Science:

  • Clinical Informatics
  • Artificial Intelligence in Medicine
  • Health Data Science

Background:

  • AI-driven Clinical Decision Support Systems (CDSSs) are moving into practice.
  • AI-CDSSs demonstrate improved diagnostic accuracy, risk stratification, resource use, and patient outcomes.
  • Traditional models are surpassed by AI-informed CDSSs in managing complex health conditions.

Purpose of the Study:

  • To examine the potential and challenges of AI-informed CDSSs in clinical practice.
  • To highlight the critical role of data accessibility and protection for AI-CDSSs.
  • To explore diverse data sources and strategies for enhancing AI-driven CDSS efficacy.

Main Methods:

  • Review of AI-informed CDSSs across various medical specialties (oncology, transplantation, etc.).
  • Analysis of opportunities and challenges in AI-CDSS implementation.
  • Discussion of public, institutional, commercial, and governmental data sources and their constraints.

Main Results:

  • AI-CDSSs offer significant improvements in patient care and clinical decision-making.
  • Limited access to high-quality, granular health data is a major barrier to AI-CDSS development.
  • Ethical and practical constraints affect the usability of available data sources.

Conclusions:

  • Overcoming data accessibility and protection issues is essential for widespread AI-CDSS adoption.
  • Strengthening health data ecosystems is key to maximizing the benefits of AI in medicine.
  • AI holds substantial promise for improving patient outcomes if data challenges are addressed.